Grant: Discovery Grants Program - Individual

Next generation 5G and beyond wireless networks will rely on software-defined intelligent radio access network (SIRAN) equipment that can be readily reconfigured to satisfy the diverse and changing needs of mobile network operators (MNOs) to provide high-quality services for a wide range of applications, from enhanced mobile broadband communications, to massive machine-type communications, to ultra-reliable low-latency communications. SIRAN enables provisioning of ""slices"" of communication and computing resources to guarantee quality of service (QoS) and users' quality of experience (QoE). Furthermore, scalable SIRANs enables heterogeneous networks (HetNets) that integrate macro-, micro-, pico- and/or femto-cells. Such unprecedented flexibility and programmability of SIRANs together with the variability and dynamicity of the network service demands, user traffic characteristics, and user location and mobility present great challenges to the efficient operation and management of SIRANs by optimizing the utilization of network resources including frequency spectrum, channel bandwidth and transmission time schedule, transmission power, computation and storage resources, and energy consumption, while satisfying the QoS/QoE requirements of services and applications. Classical modeling and optimization techniques have difficulty dealing with future multi-service HetNets when multiple operational parameters need to be simultaneously optimized while system conditions are dynamically changing. Our overall objective is to fill this gap by developing techniques to manage in real-time the allocation of SIRAN resources (e.g., communication, caching, computing). We will leverage contemporary machine learning, particularly deep learning techniques to optimize resource utilization while satisfying the required QoS/QoE. Our proposed techniques and solutions will enable autonomous and intelligent network service provisioning that takes advantage of the programmability of SIRANs. We shall develop both model-free as well as combined modeling/model-free techniques, driven by deep-learning engines to quickly adapt system operation towards the desired optimal operation region based on service requirements under dynamically varying network and user traffic conditions. The techniques developed in this project will form the basis of future industry-partnership projects in collaboration with MNOs to collect network data that enables these techniques to be evaluated based on practical network conditions, and to develop testbeds for experimental verification of our work and proof-of-concept technology transfer. This project will provide an excellent opportunity to train the next generation wireless networking engineers and researchers who are knowledgeable on the use of contemporary machine intelligence techniques to address the complexity and dynamic nature of the next generation wireless networks.